No feasible solution in seasonal ARIMA process

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Hello everyone!
I created a seasonal ARIMA process and when I run it, I get the following output/warning. It says no feasible solution can be found... Does anyone know how to get rid of this problem?
.......
__________________________________________________________ End diagnostic information
Local minimum found that satisfies the constraints.
Optimization completed because the objective function is non-decreasing in feasible directions, to within the selected value of the function tolerance, and constraints are satisfied to within the selected value of the constraint tolerance.
Warning: Nonlinear inequality constraints are active; standard errors may be inaccurate. > In arima.estimate at 1018 In austesten_sarima at 40
ARIMA(3,1,2) Model Seasonally Integrated with Seasonal AR(3) and MA(2):
--------------------------------------------------------------------------
Conditional Probability Distribution: Gaussian
Standard t
Parameter Value Error Statistic
----------- ----------- ------------ -----------
Constant 0 Fixed Fixed
AR{1} 0.863241 0.0692327 12.4687
AR{2} -0.171672 0.0765376 -2.24298
AR{3} 0.115093 0.0421485 2.73065
SAR{1} 0.857417 0.564547 1.51877
SAR{2} 0.292977 0.575573 0.509018
SAR{3} -0.192652 0.210905 -0.913454
MA{1} -0.409755 0.297717 -1.37632
MA{2} -0.580543 0.295877 -1.96211
SMA{1} -1.35118 0.423402 -3.19126
SMA{2} 0.351184 0.42345 0.82934
Variance 0.822403 0.00364983 225.326
__________________________________________________________ Diagnostic Information
Number of variables: 12
Functions Objective: @(X)nLogLike(X,YData,XData,E,V,OBJ,AR.Lags,MA.Lags,maxPQ,T,isDistributionT,options,userSpecifiedY0,userSpecifiedE0,userSpecifiedV0,trapValue) Gradient: finite-differencing Hessian: finite-differencing (or Quasi-Newton) Nonlinear constraints: @(x)nonLinearConstraints(x,LagsAR,LagsSAR,LagsMA,LagsSMA,tolerance) Nonlinear constraints gradient: finite-differencing
Constraints Number of nonlinear inequality constraints: 4 Number of nonlinear equality constraints: 0
Number of linear inequality constraints: 0 Number of linear equality constraints: 1 Number of lower bound constraints: 12 Number of upper bound constraints: 12
Algorithm selected sequential quadratic programming
__________________________________________________________ End diagnostic information
No feasible solution found.
fmincon stopped because the size of the current step is less than the default value of the step size tolerance but constraints are not satisfied to within the selected value of the constraint tolerance.
Warning: Nonlinear inequality constraints are active; standard errors may be inaccurate. > In arima.estimate at 1018 In austesten_sarima at 40 Error using arima/validateModel (line 1298) The non-seasonal moving average polynomial is non-invertible.
Error in arima/setLagOp (line 401) OBJ = validateModel(OBJ);
Error in arima/estimate (line 1088) OBJ = setLagOp(OBJ, 'MA' , LagOp([1 coefficients(iMA)' ], 'Lags', [0 LagsMA ]));
Error in austesten_sarima (line 40) [fit,EstParamCov,LogL,info] = estimate(model,Y,'options',options);

Accepted Answer

Matt J
Matt J on 28 Sep 2014
It says in the documentation that the 'MA' parameter must be a "cell vector of nonseasonal moving average coefficients corresponding to an invertible polynomial."
However, one of your error messages says
In austesten_sarima at 40 Error using arima/validateModel (line 1298) The non-seasonal moving average polynomial is non-invertible.
  2 Comments
MC3105
MC3105 on 28 Sep 2014
I know that this is a problem.. although my question is: How can I solve it? How can I assure that my SMA polynomial is non-invertible.
The funny thing is, that this does not happen at every run-through, but only for certain input data (it's a sliding window prediction).
Matt J
Matt J on 28 Sep 2014
Edited: Matt J on 28 Sep 2014
Not sure, but it's not really a MATLAB issue. It's an issue in the theory of ARIMA and how you've chosen your model parameters. However, Rule 10 in this document might help,

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